Biostatistics Student Seminar: Aaron Hudson

Presentation: An Approximate Hypothesis Test for Absence/Presence Qualitative Interactions with Applications to Network Inference

Speaker: Aaron Hudson, Graduate Student, UW Biostatistics

Abstract: In biomedical applications, it is often of interest to study how the association among many variables in a population differs by sub-population membership. For example, an objective of many studies of functional brain connectivity is to determine how temporal dependence between physically distinct brain regions is differs between a population with a neurodegenerative disease and a healthy population. Commonly, practitioners assess for differential association by performing hypothesis tests for quantitative interactions – present when absolute differences in measures of association are non-zero. However, there are many settings in which quantitative interactions may not be clinically meaningful – for instance, when the units of the variables of interest are not interpretable. Alternatively, one may prefer to perform hypothesis tests for qualitative interactions – present when measures of association differ in sign. When a measure association is positive in population negative in another, we say a positive/negative interaction exists; when a measure of association is non-zero in one population and zero in another, we say an absence/presence interaction exists. Considerable work has been conducted to develop hypothesis tests for positive/negative interactions, though there is limited work on hypothesis testing for absence/presence interactions. Existing methods to test for absence/presence interactions require strong assumptions on minimum signal strength for type-I error control. To relax this strong assumption, we propose an alternative method which approximately tests for absence/presence interactions by testing the approximately equivalent null hypothesis that the relative difference in measures of association is bounded by a large pre-specified constant. Our proposed method ensures asymptotic type-I error control without minimum signal-strength assumptions. We compare our procedure to existing methods in simulations and apply our method to network inference in the analysis of resting state functional neuroimaging data.

Wed, Feb 13, 2019, 3:30pm to 5:00pm
Room F-643 (HSF)